A component that fits and predicts given data.
To implement a new Estimator, define your own class which is a subclass of Estimator, including
a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters).
Define an __init__ method which sets up any necessary state and objects. Make sure your __init__ only
uses standard keyword arguments and calls super().__init__() with a parameters dict. You may also override the
fit, transform, fit_transform and other methods in this class if appropriate.
To see some examples, check out the definitions of any Estimator component.
Constructs a new component with the same parameters and random state.
Describe a component and its parameters
Fits component to data
Loads component at file path
Make predictions using selected features.
Make probability estimates for labels.
Saves component at file path